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Journal Article

High-resolution climate projection dataset based on CMIP6 for Peru and Ecuador: BASD-CMIP6-PE

Authors
/persons/resource/fernandez.carlos

Fernandez Palomino,  Carlos Antonio
Potsdam Institute for Climate Impact Research;

/persons/resource/Fred.Hattermann

Hattermann,  Fred Fokko
Potsdam Institute for Climate Impact Research;

/persons/resource/Valentina.Krysanova

Krysanova,  Valentina
Potsdam Institute for Climate Impact Research;

Vega-Jácome,  Fiorella
External Organizations;

/persons/resource/Christoph.Menz

Menz,  Christoph
Potsdam Institute for Climate Impact Research;

/persons/resource/Stephanie.Gleixner

Gleixner,  Stephanie
Potsdam Institute for Climate Impact Research;

Bronstert,  Axel
External Organizations;

External Ressource
Fulltext (public)

s41597-023-02863-z.pdf
(Publisher version), 10MB

Supplementary Material (public)
There is no public supplementary material available
Citation

Fernandez Palomino, C. A., Hattermann, F. F., Krysanova, V., Vega-Jácome, F., Menz, C., Gleixner, S., Bronstert, A. (2024): High-resolution climate projection dataset based on CMIP6 for Peru and Ecuador: BASD-CMIP6-PE. - Scientific Data, 11, 34.
https://doi.org/10.1038/s41597-023-02863-z


Cite as: https://publications.pik-potsdam.de/pubman/item/item_29290
Abstract
Here, we present BASD-CMIP6-PE, a high-resolution (1d, 10 km) climate dataset for Peru and Ecuador based on the bias-adjusted and statistically downscaled CMIP6 climate projections of 10 GCMs. This dataset includes both historical simulations (1850–2014) and future projections (2015–2100) for precipitation and minimum, mean, and maximum temperature under three Shared Socioeconomic Pathways (SSP1-2.6, SSP3-7.0, and SSP5-8.5). The BASD-CMIP6-PE climate data were generated using the trend-preserving Bias Adjustment and Statistical Downscaling (BASD) method. The BASD performance was evaluated using observational data and through hydrological modeling across Peruvian and Ecuadorian river basins in the historical period. Results demonstrated that BASD significantly reduced biases between CMIP6-GCM simulations and observational data, enhancing long-term statistical representations, including mean and extreme values, and seasonal patterns. Furthermore, the hydrological evaluation highlighted the appropriateness of adjusted GCM simulations for simulating streamflow, including mean, low, and high flows. These findings underscore the reliability of BASD-CMIP6-PE in assessing regional climate change impacts on agriculture, water resources, and hydrological extremes.